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The nature of nonlinearity owned by process is often
become the constraint for conventional control systems applying which have a linear characteristics. The limitation of design controller such as linearization, small range operation can make the performance of control system become unwell.The effort to increase performance of control systems is developing the nonlinear control system that in applying control system based on the model by using nonlinear model. It has been developed a nonlinear control system using predictive control system algorithm based on artificial neural network in this research. The main element of predictive control systems contain of a process model, objective function, and optimization of system. Model is used to predict process output as long as a prediction horizon. Objective function used to represent control system performance, while optimization system used to determine the control signaIs which can minimalist objective function The nonlinear model obtained by using the artificial neural network. The artificiaL neural network structures use Multilayer Perceptron (MLp) with the Levenberg Moryud training algoritlm. This artificial neural network able to noful the pr@ess of hea etthanger by RIwISE: 0.0057. The Prediaive control systemsd evebped by using Quasi Newton of Optimimtion algoritlxn which is uwd to get the corilrol signl to be pasred to the pruess According to the simulations, which have been accomplished, so thot the p&anebr of predictive confrol systcm by onltne can be obtaiwd at the heat erchfrtger process of PCT|3. The reslts tndicate that the predictive control systems based on ke dificial rmral network able to overcome the nature of nonlinewity owned by process utd give the good performance with the following ualue : rise time 8r) = 70 secon4 mmirrum wershoot (Mp)--2.4%, rettlingtine (Ts) = 94 recondmdenor suady stde (Els) = 2.4% Keywords : Predictive Control, Nearal Networks, Optimiution, Quasi Newbn Algoritlm, PCT I 3. lebih jelas lihat di abstract en

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